Systems and methods for calibrating display systems

ABSTRACT

A method of calibrating a display system, the method comprising: displaying a test pattern including a plurality of blobs; detecting one or more base blobs in the displayed test pattern; identifying, based on the detected base blobs, patches of the test pattern, wherein each patch comprises one of the base blobs and a subset of additional blobs detected in the displayed test pattern; determining a patch location for at least one patch within the test pattern based on the subset of the additional blobs in the patch to determine a blob location for at least one detected blob; determining a calibration parameter for the display system based on the blob location and a detected attribute of the at least one detected blob; and calibrating the projector using the calibration parameter.

FIELD

The specification relates generally to display systems, and moreparticularly to systems and methods for calibrating display systems.

BACKGROUNDS

Display systems, such as systems with one or more projectors, cameras,or display devices, may be employed to project videos and images on avariety of different surfaces. However, the surfaces may be uneven, havetheir own coloring and/or imperfections, or the display devices may bemisaligned and/or otherwise introduce imperfections and distortionswhich causes the projected image to appear distorted or otherwiseinaccurate to the desired image.

SUMMARY

According to an aspect of the present specification, an example methodof calibrating a display system includes: displaying a test patternincluding a plurality of blobs; detecting one or more base blobs in thedisplayed test pattern; identifying, based on the detected base blobs,patches of the test pattern, wherein each patch comprises one of thebase blobs and a subset of additional blobs detected in the displayedtest pattern; determining a patch location for at least one patch withinthe test pattern based on the subset of the additional blobs in thepatch to determine a blob location for at least one detected blob;determining a calibration parameter for the display system based on theblob location and a detected attribute of the at least one detectedblob; and calibrating the display system using the calibrationparameter.

According to another aspect of the present specification, an examplesystem includes: a display system configured to display a test patternonto a surface, the test pattern including a plurality of blobs; acamera configured to capture an image of at least a portion of thedisplayed test pattern; and a processor configured to: detect one ormore base blobs in the test pattern; identify, based on the detectedbase blobs, a patch of the test pattern, wherein the patch comprises oneof the base blobs and a subset of additional blobs detected in the testpattern; determine a patch location for the patch within the testpattern based on the subset of the additional blobs in the patch todetermine a blob location for at least one detected blob; determine acalibration parameter for the display system based on the blob locationand a detected attribute of at least one detected blob; and calibratethe display system using the calibration parameters.

According to another aspect of the present specification, anotherexample method of calibrating a display system includes: displaying atest pattern including a plurality of blobs; identifying patches of thetest pattern, wherein each patch comprises a subset of blobs detected inthe displayed test pattern; determining a patch location for at leastone patch within the test pattern based on the blobs in the patch;determining a blob location for each at least one detected blob in thepatch based on the patch location; determining a calibration parameterfor the display system based on the blob location and a detectedattribute of each the at least one detected blob; and calibrating thedisplay system using the calibration parameter.

BRIEF DESCRIPTION OF DRAWINGS

Implementations are described with reference to the following figures,in which:

FIG. 1 depicts a block diagram of an example system for calibrating adisplay system;

FIG. 2 depicts a schematic diagram of an example patch of a test patternfor calibrating a display system;

FIG. 3 depicts a block diagram of certain internal components of theprojector of FIG. 1 ;

FIG. 4 depicts a flowchart of an example method of calibrating a displaysystem;

FIG. 5 depicts a flowchart of an example method of detecting base blobsat block 415 of the method of FIG. 4 ;

FIG. 6 depicts a flowchart of an example method of determining a patchlocation at block 425 of the method of FIG. 4 ;

FIG. 7 depicts a flowchart of an example method of verifying a patchaddress using hyper-addressing;

FIG. 8 depicts a schematic diagram of an example macro-patch of a testpattern for calibrating a display system;

FIG. 9 depicts a flowchart of an example method of determining acalibration parameter at block 430 of the method of FIG. 4 ;

FIG. 10 depicts a flowchart of an example method of adjusting a cameraparameter; and

FIG. 11 depicts a flowchart of an example method of adjusting a displaysystem parameter.

DETAILED DESCRIPTION

To compensate for the effects of the surface onto which projectorsand/or other display devices display images and videos, the input datamay be adjusted by the display system to calibrate the output to betterapproximate the target. To calibrate the display system and/or to alignone or more projectors or display devices relative to each other, thedisplay system may display a test pattern onto the surface. An image ofthe displayed test pattern, or at least a portion of the displayed testpattern may be captured by a camera and the image analyzed to determinehow to calibrate the display system. Often, in order to betterdifferentiate between different shades and hues of various colors and/orto gather data required for modelling, multiple test patterns arerequired, which may result in the calibration process beingtime-consuming and inconvenient when initially setting up a projector.

An example test pattern in accordance with the present specificationincludes a plurality of blobs arranged in patches, with each patchhaving a white base blob defining the patch, and red, green, and bluereference blobs. The arrangement of the blobs in the patch and theinclusion of white, red, green and blue blobs allows the test pattern tobe used for color compensation, geometric alignment, and luminancecorrection, with a single-shot test pattern. In particular, to calibratethe display system, a processor may detect the white base blob in theprojected pattern, identify patches of the test pattern based on thebase blob, identify the reference blobs in the patches and use thereference blobs to decode the colors of the additional blobs in thepatch. Further, the test pattern may be arranged such that the colors ofeach of the additional blobs in the patch defines a patch address thatallows the processor to locate the patch within the test pattern. Thus,the processor may use the location of the patch to accurately compare atarget attribute (i.e., the input to the test pattern) and a detectedattribute (i.e., as displayed on the surface) of a given blob, andcompensate or apply a calibration parameter as appropriate.

FIG. 1 depicts a system 100 for calibrating a display system, such as aprojector 104. The present example will be described in conjunction withthe projector 104, however it will be understood that calibration ofother suitable display systems and devices is also contemplated. Theprojector 104 is configured to project a test pattern 108 onto a surface112. The system 100 may further include a camera 116 (e.g., an opticalcamera) to capture an image of the projected test pattern 108. Thecamera 116 may be a discrete component of the system 100, as shown, orthe camera 116 may be integrated into the projector 104. The image ofthe projected test pattern 108 captured by the camera 116 may then beanalyzed to identify calibration parameters for the projector 104 inorder to calibrate the projector 104 with respect to the surface 112.For example, the calibration parameters may be to adjust the color,luminance, geometric alignment, distortion, color convergence, focus, orthe like, to allow the projector 104 to subsequently project otherimages or videos with high clarity, contrast, and appropriate color ontothe surface 112.

Accordingly, the test pattern 108 includes features to facilitate thecalibration of the projector 104 with respect to color, luminance,geometric alignment, distortion, focus, color convergence, and the like.The test pattern 108 may further allow the projector 104 to becalibrated for focus and exposure. In particular, the test pattern 108is formed of a plurality of blobs 120, each of which is a region of agiven color. The blobs 120 may be squares, circles, other geometricshapes, or other suitable forms. Further, each of the blobs 120 may havethe same form as the other blobs 120, or the blobs 120 may havedifferent forms. The blobs 120 of the test pattern 108 may be organizedto form patches 124. Each patch 124 includes a subset of the blobs 120and has certain properties for use in the calibration of the projector104, as will be further described below.

For example, referring to FIG. 2 , an example patch 124 is depicted. Inthe present example, the patch 124 includes nine blobs 120, arranged ina three-by-three grid. In particular, the nine blobs 120 include a baseblob 200, three reference blobs 204-1, 204-2, 204-3 (referred to hereingenerically as a reference blob 204 or collectively as reference blobs204; this nomenclature is also used elsewhere herein), and fiveadditional blobs 208-1, 208-2, 208-3, 208-4, 208-5.

The base blob 200 is a blob which may be used to identify the patch 124from the detected blobs from the projected test pattern 108. Inparticular, the blobs 120 forming the patch 124 have a certainpredefined spatial relationship to the base blob 200. For example, giventhe base blob 200, the patch 124 may be defined as the base blob 200 andthe eight nearest neighbor blobs to the base blob 200 (i.e., the fourblobs directly adjacent to the base blob 200 and the four blobsdiagonally adjacent to the base blob 200, such that the base blob 200 isin the center of the three-by-three array of blobs). That is, each patch124 may include a base blob 200 at the center of the patch 124. In otherexamples, other spatial relationships of the base blob 200 and the patch124 are contemplated.

Accordingly, since the base blob 200 is used to identify the patch 124,the base blob 200 may be selected to have a distinctive color or otherfeature detectable in the projected test pattern 108, and consistentlydistinguishable from the other blobs 120 in the test pattern 108. In thepresent example, the base blob 200 is white in color, and hence will bethe brightest or most intense detected blob, in particular amongst itseight nearest neighbors. In other examples, the base blob 200 may have adistinct shape, or may be additionally distinguished based on thesurrounding blobs.

The reference blobs 204 are blobs in the patch which may be used aspoints of reference to orient the patch 124 and/or as color reference toenable color calibration of the projector 104, particularly on adversesurfaces, or for other reference purposes for further calibrating theprojector 104. In the present example, the first reference blob 204-1 islocated in the top left corner of the three-by-three array of blobs inthe patch 124, the second reference blob 204-2 is located in the topright corner of the three-by-three array of blobs in the patch 124, andthe third reference blob 204-3 is located at the bottom center of thethree-by-three array of blobs in the patch 124. Further, in the presentexample, the first reference blob 204-1 is a red blob, the secondreference blob 204-2 is a green blob, and the third reference blob 204-3is a blue blob. In other examples, the reference blobs 204 may beselected to have other distinguishable colors or features. Thecombination of the designated locations and colors of each of thereference blobs 204 causes the patch 124 to be rotationally andreflectively asymmetric, and hence the reference blobs 204 may be used,for example, to determine the orientation of the test pattern (i.e.,since the red reference blob 204-1 is in the top left corner, relativeto the white blob 200, etc.), as well as whether the projector 104 is afront projector or a rear projector.

Further, since the reference blobs 204 in the present example cover thethree primary colors of red, green and blue, the reference blobs 204 maybe used as references for color identification and correction. Inparticular, the red, green and blue reference blobs 204 may be assumedto be the closest in hue to the original red, green and blue colors, andonly suffer from intensity issues. Accordingly, their appearance on thesurface 112 may be used as a reference for the appearance of othercolors with red, green, and blue hues on the surface 112.

The five additional blobs 208 are other blobs which define a patchaddress 212 for the patch 124. For example, the five additional blobs208 may be colored or greyscale blobs. Preferably, the colors of theadditional blobs 208 may be selected from a predefined list of blobcolors. The blob colors may be, for example, the secondary and tertiarycolors. Based on the spatial relationships of the additional blobs 208to the reference blobs 204, the additional blobs 208 may be ordered toform an ordered list. For example, in the present example, theadditional blob 208-1 adjacent the blue reference blob 204-3 and in thesame column as the red reference blob 204-1 is designated as the firstadditional blob 208-1. The remaining additional blobs 208 may besequentially ordered by moving clockwise (i.e., towards the redreference blob 204-1 and away from the blue reference blob 204-3)through the additional blobs 208 to achieve an ordered list. The colors,C₁, C₂, C₃, C₄, and C₅ of the additional blobs 208 in their given orderdefine the patch address 212. In other examples, other pre-definedorders of the color blobs, as defined relative to the base blob 200 andthe reference blobs 294, including other sufficiently large subsets ofthe color blobs to uniquely identify the patch 124, are alsocontemplated.

It will be appreciated that in other examples, the patch 124 may includea different number of blobs 120, different configurations of the blobs120, different colors or properties for the base blob 200 and thereference blobs 204, and the like. For example, the patch 124 could bean array of a different size, hexagonally tiled, or use an arrangementother than the base blob 200 and the three reference blobs 204.Additionally, in some examples, the patch 124 need not include the baseblob 200 and/or the three reference blobs 204 and may instead beidentifiable based on another arrangement and/or relationship betweenthe blobs 120 forming the patch 124.

Referring to FIG. 3 , certain internal components of the projector 104are depicted in greater detail. The projector 104 includes a controller300 and a memory 304. The projector 104 may further include acommunications interface 308 and optionally, an input/output device (notshown).

The controller 300 may be a processor such as a central processing unit(CPU), a microcontroller, a processing core, or similar. The controller300 may include multiple cooperating processors. In some examples, thefunctionality implemented by the controller 300 may be implemented byone or more specially designated hardware and firmware components, suchas a field-programmable gate array (FPGA), an application-specificintegrated circuit (ASIC), a graphics processing unit (GPU), or thelike. In some examples, the controller 300 may be a special purposeprocessor which may be implemented via the dedicated logic circuitry ofan ASIC or FPGA to enhance the processing speed of the calibrationoperation discussed herein.

The controller 300 is interconnected with a non-transitorycomputer-readable storage medium, such as the memory 304. The memory 304may include a combination of volatile memory (e.g., random access memoryor RAM), and non-volatile memory (e.g., read only memory or ROM,electrically erasable programmable read only memory or EEPROM, flashmemory). The controller 300 and the memory 304 may comprise one or moreintegrated circuits. Some or all of the memory 304 may be integratedwith the controller 300.

The memory 304 stores a calibration application 316 which, when executedby the controller 300, configures the controller 300 and/or theprojector 104 to perform the various functions discussed below ingreater detail and related to the calibration operation of the projector104. In other examples, the application 316 may be implemented as asuite of distinct applications. The memory 304 also stores a repository320 configured to store calibration data for the calibration operation,including a list of blob colors used in the test pattern, a list ofvalid addresses used in the test pattern, a list of hyper-addresses usedin the test pattern, and other rules and data for use in the calibrationoperation of the projector 104.

The communications interface 308 is interconnected with the controller300 and includes suitable hardware (e.g., transmitters, receivers,network interface controllers and the like) allowing the projector 104to communicate with other computing devices. The specific components ofthe communications interface 308 are selected based on the type ofnetwork or other links that the projector 104 is to communicate over.For example, the communications interface 308 may allow the projector104 to receive images of the projected test pattern from the camera 116,in examples where the camera 116 is not integrated with the projector104.

The operation of the system 100 will now be described in greater detailwith reference to FIG. 4 . FIG. 4 depicts a flowchart of an examplemethod 400 of calibrating a projector. The method 400 will be describedin conjunction with its performance in the system 100, and in particularvia execution of the application 316 by the processor 300, withreference to the components illustrated in FIGS. 1-3 . In otherexamples, some or all of the method 400 may be performed by othersuitable devices, such as a media server, or the like, or in othersuitable systems.

At block 405, the projector 104 projects the test pattern 108 onto thesurface 112 and the camera 116 captures an image of the test pattern 108as projected onto the surface 112. In particular the image captured bythe camera 116 represents the appearance of the test pattern 108 on thesurface 112, including any geometric deformation, color distortion, andthe like, which appear as a result of the properties of the surface 112.Additionally, at block 405, in examples where the camera 116 is distinctfrom the projector 104, the camera 116 may transmit the captured imageto the projector 104 for further processing, and in particular to allowthe processor 300 to compute calibration parameters for the projector104. In further examples, rather than computing the calibrationparameters at the projector 104, the calibration parameters may becomputed at a separate computing device, such as a connected laptop ordesktop computer, a server, or the like. Accordingly, in such examples,the camera 116 may transmit the captured image to the given computingdevice to compute the calibration parameters for the projector 104.

At block 410, the processor 300 analyzes the captured image to detectthe blobs 120 of the test pattern 108. The blobs 120 may be detectedusing standard computer vision techniques, using convolution,differential methods, local extrema, or the like.

At block 415, the processor 300 detects one or more base blobs in theprojected test pattern 108. In particular, the processor 300 mayidentify, from the blobs 120 detected at block 410, which blobs 120satisfy the criteria of the base blobs and designate a subset of theblobs 120 as base blobs. For example, referring to FIG. 5 , an examplemethod 500 of identifying base blobs from the projected test pattern 108is depicted. In particular, the method 500 will be described inconjunction with identifying base blobs in the test pattern 108 havingpatches 124, in particular, organized in the manner described inconjunction with FIG. 2 . It will be understood that in other exampleswhere the base blobs has other identifying characteristics (e.g.,shape), other methods of identifying the base blob are contemplated.

At block 505, the processor 300 selects a blob 120 to analyze todetermine whether or not it is a base blob. Accordingly, the processor300 may select a blob 120 detected at block 410 which has not beenvalidated as a base blob or invalidated as a base blob.

At block 510, the processor 300 identifies neighboring blobs 120 of theblob 120 selected at block 505. For example, when the base blobs arelocated in the center of a patch 124, the processor 300 may retrieve theeight nearest neighbors of the selected blob 120. Preferably, the testpattern 108 may be arranged such that adjacent blobs 120 are spacedapart by a predefined amount. For example, the space between adjacentblobs 120 may be about half the width of a blob 120. Accordingly, theprocessor 300 may look for blobs 120 detected at block 410 which arewithin 2.5 widths of the selected blob 120 to identify the neighbors ofthe selected blob 120.

At block 515, the processor 300 selects one of the neighbors identifiedat block 510 for comparison against the blob 120 selected at block 505.In particular, the processor 300 may select a neighboring blob 120 whichhas not yet been compared to the selected blob.

At block 520, the processor 300 compares the intensity of the selectedneighboring blob 120 to the selected blob 120. For example, theprocessor 300 may sum the red, green and blue (RGB) components of theselected neighboring blob 120 and the selected blob 120 and compare thetwo sums. To obtain the RGB component values, the processor 300 maysample RGB components of the given blob 120 at its center, at apredefined set of coordinates within the blob 120, or the processor 300may average the RGB components over the blob 120, or other suitablemethods of obtaining RGB component values over the blob 120. Inparticular, since the base blobs in the present example are white incolor, the processor 300 may determine whether the intensity (i.e., thesum of the RGB components) of the selected blob 120 is greater than theintensity of the selected neighboring blob 120.

If the decision at block 520 is negative, that is that the intensity ofthe selected blob 120 is not greater than the intensity of the selectedneighboring blob 120, then the processor 300 proceeds to block 535. Atblock 535, the processor 300 invalidates the blob 120 selected at block505 as a potential base blob. That is, since there is at least oneneighboring blob 120 which is more intense than the selected blob 120,the processor 300 may deduce that the selected blob 120 is not a whiteblob, since neighboring blobs 120 are likely to suffer from similarcolor distortions, and hence the white blobs would remain more intensethan their neighbors. Accordingly, the processor 300 may conclude thatthe selected blob 120 is not a base blob in the test pattern 108. Theprocessor 300 may subsequently return to block 505 to continue selectingblobs 120 to identify the base blobs in the test pattern 108.

If the decision at block 520 is affirmative, that is that the intensityof the selected blob 120 is greater than the intensity of the selectedneighboring blob 120, then the processor 300 proceeds to block 525. Atblock 525, the processor 300 may invalidate the neighboring blob 120selected at block 515 as a base blob, since it has at least oneneighboring blob 120 (namely, the selected blob 120) which is moreintense than it. Further, the processor 300 determines whether or notthe selected blob 120 has more neighboring blobs 120.

If the decision at block 525 is affirmative, that is, that the selectedblob 120 has more neighboring blobs 120 against which its intensity hasnot yet been compared, the processor 300 returns to block 515 to selecta further neighboring blob 120 to compare intensities.

If the decision at block 525 is negative, that is, that the selectedblob 120 has no more neighboring blobs 120 against which its intensityhas not yet been compared, the processor 300 proceeds to block 530. Atblock 530, the processor 300 validates the blob 120 selected at block505 as a base blob. That is, having determined that the selected blob120 has a higher intensity than each of its neighbors, the processor 300may therefore determine that the selected blob 120 is white in color andtherefore a base blob 200. The processor 300 may then return to block505 to continue assessing blobs 120 to find the base blobs 200 in thetest pattern 108.

Returning to FIG. 4 , at block 420, after detecting the base blobs 200in the test pattern 108, the processor 300 uses the base blobs 200 toidentify the patches 124. For example, the processor 300 may define apatch 124 as a base blob 200 and the eight nearest neighboring blobs 120of the base blob 200, for each base blob 200 identified at block 415.

In some examples, in addition to identifying base blobs 200 as being themost intense blobs amongst their eight nearest neighbors, the processor300 may additionally verify a candidate blob as a base blob 200 based onthe arrangement of the other blobs 120 within the patch 124 defined bythe candidate blob. For example, the processor 300 may identify, withinthe patch 124, a red blob, a green blob, and a blue blob. In otherexamples, the processor 300 may select different color reference blobs.The processor 300 may additionally verify that the red, green and blueblobs are located in the patch 124 relative to the base blob 200 and toone another based on the predefined configurations of the patch 124. Insome examples, if the red, green and blue blobs identified in the patch124 do not satisfy the predefined configuration of the patch 124, theprocessor 300 may determine that the candidate white blob is not in facta valid base blob 200.

In further examples, the processor 300 may omit block 415 entirely, andhence at block 420, may identify the patches solely on the basis of theblobs in the patch. Thus, for example, the processor may select a groupof blobs in a sliding window (e.g., a 3×3 array, a 2×2 array, orotherwise selected based on the size and shape of an expected patch).The group of blobs may be compared against a list of valid patches, andthe groups whose colors and positions match a valid patch may beidentified as a patch. The processor 300 may reject groups of blobswhich are made from partial groups of multiple patches. In particular,such an identification mechanism when each patch in the test pattern isunique.

At block 425, the processor 300 determines the patch location for atleast one of the patches 124 identified at block 420. In some examples,the processor 300 may identify the patch location for all the patches124 identified at block 420, while in other examples, the processor 300may select a subset of patches 124 for which to identify the patchlocation. The subset may be selected, for example based on the spatialarrangement of the patches 124 (e.g., the location of each patch in analternating or checkerboard pattern or the like) or other suitableselection criteria. The processor 300 may use the eight nearestneighboring blobs 120 in the patch 124 to determine the patch locationbased on predefined configurations and properties of each patch 124.Alternately, the processor 300 may use a suitable subset of blobs 120 inthe patch 124 which uniquely identify the patch 124 and allow the patch124 to be located in the test pattern. For example, referring to FIG. 6, a flowchart of an example method 600 of determining a patch locationis depicted.

At block 605, the processor 300 selects a patch 124 to locate. Inparticular, the patch 124 may be selected based on its base blob 200.

At block 610, the processor 300 may identify the reference blobs 204 inthe patch 124. For example, when the reference blobs 204 are red, greenand blue blobs, the processor 300 may identify the reference blobs 204by selecting the blobs 124 which have RGB components which are closestto a red hue, a green hue, and a blue hue, respectively. For example,the processor 300 may use a least-squares method, a cosine distance, orother suitable method to determine the distance of the color (i.e.,based on its RGB components) of a given blob 120 to the RGB componentvalues of a red blob. The blob 120 in the patch 124 which is closest toa red color may be determined by the processor 300 to be the redreference blob 204-1. Similarly, the processor 300 may identify theblobs 120 in the patch 124 which are closest to a green color and a bluecolor as the green reference blob 204-2 and the blue reference blob204-3, respectively. Additionally, having identified the reference blobs208, the processor 300 may also identify the remaining blobs 120 of thepatch 124 as additional blobs 208.

At block 615, the processor 300 orders the additional blobs 208 into anordered list. To do so, the processor 300 may first orient the patch 124using the reference blobs 204. For example, the designated locations ofthe reference blobs 204 may cause the patch 124 to be rotationally andreflectively asymmetrical, and hence the processor 300 may use the redreference blob 208-1 to define the top left corner of the patch 124, andthe green reference blob 208-2 to define the top right corner of thepatch 124. The processor 300 may additionally confirm the orientation ofthe patch 124 by verifying that the blue reference blob 208-3 is in thebottom center.

Having oriented the patch 124, the processor 300 may sort the additionalblobs 208 based on their location in the patch 124 to identify theirposition in the ordered list. In particular, since the patch 124 isoriented, the additional blob 208 in the bottom left corner may bedesignated as the first additional blob 208-1. The additional blobs 208may then be added to the ordered list proceeding in a clockwisedirection, from the first additional blob 208-1. Thus, the additionalblob 208 immediately above the first additional blob 208-1 may bedesignated as the second additional blob 208-2. In particular, theordered list as generated from a specific, predefined orientation of thepatch 124 allows the additional blobs 208 to encode a patch address,without risk of duplicates based on using the same additional blobs 208in a different order for a different patch 124. Thus, the ordered listof additional blobs 208 in the example patch 124 depicted in FIG. 2 is[208-1, 208-2, 208-3, 208-4, 208-5].

Having generated an ordered list of the additional blobs 208, theprocessor 300 may then determine the patch address 212 for the patch124. In particular, at block 620, the processor 300 selects anadditional blob 208 from the ordered list. The additional blob 208 maybe the next additional blob 208 which has not yet been processed togenerate the patch address 212. Thus, the processor 300 may begin withthe first additional blob 208-1 at the first iteration of block 620.

At block 625, the processor 300 determines the color of the additionalblob 208 selected at block 620. In particular, the processor 300predicts the intended target color (i.e., the input color) of theselected additional blob 208. That is, rather than simply taking thecolor of the additional blob 208 as projected onto the surface 112, theprocessor 300 may use the RGB component values of the white base blob200 and the red, green and blue reference blobs 204 to predict the inputcolor for the selected additional blob 208. For example, if the bluecomponent value of the additional blob 208 is similar to the bluecomponent value of the blue reference blob 204-3, the processor 300 maypredict that the input blue component value of the additional blob 208is similar to the input blue component value of the blue reference blob204-3, that is, 255. Similarly, the processor 300 may scale the otherdetected RGB component values of the additional blob 208 according tothe detected RGB component values of the reference blobs 204 and thebase blob 200 to predict the other input RGB component values of theadditional blob and hence decode the input blob color of the additionalblob. More specifically, the prediction may include scaling and/oradjusting the values of the detected blobs 208 to adjust for variationsin background or ambient light to allow decoding of the input blob colorto be more accurate.

In some examples, the processor 300 may additionally verify thepredicted input color against a predefined list of blob colors used inthe test pattern 108 stored in the memory 304. That is, rather thanusing combinations of any and/or all colors (i.e., all RGB componentvalues), the test pattern 108 may contain blobs 120 with colors selectedfrom the predefined list of blob colors. For example, the predefinedlist of blob colors may include the secondary and tertiary colors. Insuch examples, the processor 300 may verify the predicted input colorand/or corrected the blob color by selecting a new predicted input colorbased on the closest blob color on the predefined list of blob colors.For example, the processor 300 may used a least-squares computation todetermine the blob color on the predefined list of blob colors which isclosest to the predicted input color and designate the closest blobcolor as the new predicted input color. In some examples, the processor300 may only designate the closest blob color as the new predicted inputcolor if the distance to the new predicted input color is below athreshold distance. Thus, if the predicted input color is mid-waybetween two possible valid blob colors, the processor 300 may deferprediction of the blob color for a more holistic verification of thepatch 124, as described below.

At block 630, the processor 300 adds the predicted input color for theblob 208 selected at block 620 to the patch address to build the patchaddress 212. In particular, since the additional blobs 208 are processedin the ordered list, the patch address 212 is similarly ordered by theassociated colors of the additional blobs 208 in the ordered list. Thus,the patch address 212 of the example patch 124 depicted in FIG. 2 is[C₁, C₂, C₃, C₄, C₅].

At block 635, the processor 300 determines whether there are any moreadditional blobs 208 in the ordered list.

If the decision at block 635 is affirmative, the processor 300 returnsto block 620 to select the next additional blob 208 in the ordered listand add its associated color to the patch address 212.

If the decision at block 640 is negative, that is, that all theadditional blobs 208 in the ordered list have been processed and theircorresponding associated colors added to the patch address, then theprocessor 300 proceeds to block 640. At block 640, the processor 300uses the patch address 212 to determine the patch location of the patch124. For example, the processor 300 may retrieve, from the memory 304, apredefined look-up table or other suitable data structure which definesa patch location associated with each patch address 212. For example,the patch location may be the coordinates of the patch 124 within thetest pattern 108. The patch location may be expressed, for example, interms of pixel coordinates of a given corner of the patch 124 (e.g., thetop left corner), pixel coordinates of a center of the patch 124,coordinates relative to other patches 124 (e.g., designating the topleft patch as 0,0), or other suitable means. In other examples, theprocessor 300 may directly compute the patch location of the patch 124based on the patch address 212 and a predefined set of rules forcomputing the patch location.

In some examples, prior to comparing the patch address 212 to thelook-up table to determine the patch location, the processor 300 mayadditionally verify the patch address against a predefined list of validpatch addresses stored in the memory 304. The predefined list of validpatch addresses includes patch addresses 212 actually employed in thetest pattern 108. That is, the list of valid patch addresses isgenerated based on the input colors to the test pattern 108.Accordingly, the test pattern 108 is preferably arranged such that eachvalid patch address appears only once on the list of valid patchaddresses. The patch addresses may thus be uniquely verified, as well asused to uniquely locate the patch 124 within the test pattern 108. Theprocessor 300 may perform verification of the patch address against thevalid patch addresses, for example based on a full matching, a partialmatching, a distance computation, or other suitable means. When thedetermined patch address is not a valid patch address, the processor 300may correct the patch address based on the list of valid patch addressesand, for example, the closest partial matching.

Further, in some examples, additionally or alternately to verifying theblob color of each of the additional blobs 208 individually, theprocessor 300 may verify the patch address 212 against the predefinedlist of valid patch addresses stored in the memory 304 based, in part orin whole, on the predicted input colors of each of the additional blobs(i.e., as opposed to the blob colors as selected from the predefinedlist of blob colors). Thus, if a predicted input color of one of theadditional blobs 208 is in between two (or more) possible blob colors onthe predefined list, verification of the patch address 212 as a wholemay allow the processor 300 to more accurately predict the correct blobcolor, particularly if one blob color corresponds with a valid patchaddress, while the other does not.

After determining the patch address for a given patch 124, the processor300 may return to block 605 to determine the patch address for anotherpatch 124, until each patch 124 associated with each base blob 200 hasbeen assigned a patch address. In some examples, after determining thepatch addresses for each patch 124, the processor 300 may additionallyvalidate the patch addresses by forming macro-patches and validating thehyper-addresses of each macro-patch. For example, FIG. 7 depicts aflowchart of an example method 700 of validating patch addresses.

At block 705, the processor 300 defines a macro-patch. The macro-patchmay be an array or subset of the patches 124 in the test pattern 108.Preferably, the macro-patch has a predefined configuration, such as atwo-by-two array, or other configuration in which the spatialrelationship between patches 124 in the macro-patch is predetermined.

At block 710, the processor 300 determines a hyper-address for themacro-patch. The hyper-address includes respective patch addresses ofthe patches forming the macro-patch. In particular, the hyper-addressfor the macro-patch may be an ordered list of the patch addresses of thepatches forming the macro-patch. Thus, to determine the hyper-addressfor the macro-patch, the processor 300 may first order the patches intoan ordered list. Since each of the patches themselves have anorientation, the macro-patch may be oriented according to theorientations of the patches forming the macro-patch. The processor 300may then select one of the patches as the first patch, according to apredefined criteria, and proceed to add patches to the list sequentiallyaccording to a predefined path between the patches of the macro-patch.The processor 300 may then define the ordered list of correspondingpatch addresses of the patches to be the hyper-address.

For example, referring to FIG. 8 , an example macro-patch 800 isdepicted. The macro-patch 800 includes four patches, 804-1, 804-2,804-3, and 804-4, arranged in a two-by-two array. In other examples,other arrangements of patches 804 in the macro-patch 800 arecontemplated. For example, the macro-patch 800 may include a largerarray of patches 804, a line of patches 804, or the like. Further, insome examples, different macro-patches may share one or more patchescontained therein.

In the present example, each of the four patches 804 has a correspondingpatch address, A₁, A₂, A₃, and A₄, respectively, defined by the blobs inthe patch 804. To generate a hyper-address 808 for the macro-patch 800,the processor 300 generates an ordered list of the patches 804. In thepresent example, the processor 300 begins at the top left patch, 804-1,and proceeds clockwise through the patches 804 in the two-by-two array.Accordingly, the ordered list of patches is [804-1, 804-2, 804-3,804-4]. The processor 300 may then generate a hyper-address 808 from theordered list of patches 804 using the corresponding patch address foreach patch 804 in the ordered list. Accordingly, the hyper-address 808is [A₁, A₂, A₃, A₄].

Returning to FIG. 7 , at block 715, the processor 300 determines whetherthe hyper-address generated at block 710 is a valid hyper-address. Forexample, the processor 300 may compare the hyper-address generated atblock 710 to a predefined list of valid hyper-addresses stored in thememory 304. The predefined list of valid hyper-addresses includeshyper-addresses actually employed in the test pattern 108, based on theinput colors and arrangement of blobs (and therefore patches) in thetest pattern 108. In particular, the valid hyper-addresses are definedbased on the predefined path through the patches in the macro-patch. Theprocessor 300 may perform the verification of the hyper-addressesagainst the valid hyper-addresses based on a full matching, a partialmatching, distance computation, or the like.

In examples where the test pattern 108 has unique patch addresses foreach patch, the hyper-addresses for each macro-patch will similarly beunique. However, in examples where patch addresses are re-used fordifferent patches at different locations in the test pattern 108, thetest pattern 108 is preferably arranged such that the hyper-addressesfor each macro-patch is unique. Uniqueness of the hyper-addresses wouldtherefore still allow the patch addresses to be uniquely verified andlocated (i.e., based on its relationship to adjacent patches in amacro-patch) within the test pattern 108.

If the determination at block 715 is affirmative, the processor 300proceeds to block 720. At block 720, the processor 300 validates each ofthe patch addresses which formed the hyper-address. That is, theprocessor 300 confirms that the patch addresses defined by the blobs ineach of the patches, is in fact the correct patch address for thatpatch.

If the determination at block 720 is negative, that is, that thehyper-address is not a valid hyper-address, the processor 300 proceedsto block 725. At block 725, the processor 300 may make a prediction asto which hyper-address is the correct hyper-address for the macro-patchand may correct the patch addresses for the patches of the macro-patch,as appropriate. For example, if three patch addresses of thehyper-address match a valid hyper-address, and the fourth patch addressis off by less than a threshold distance (e.g., as computed based on thedifferences in RGB components of the colors defining the patch address)of the fourth patch address of the valid hyper-address, then theprocessor 300 may determine that the fourth patch address should be thepatch address defined in the valid hyper-address and may correct thefourth patch address accordingly.

As will be appreciated, other verification and matching scenarios anddistance computations are also contemplated. Further, recursivelygrouping macro-patches and obtaining addresses for the groupedmacro-patches may also allow for repetition of patch addresses andhyper-addresses and/or provide further confirmation or verification ofthe correct patch addresses and hyper-addresses.

Returning to FIG. 4 , after determining the patch location at block 425,and optionally verifying the patch location, the processor 300 maysubsequently use the patch location to determine a blob location for atleast one detected blob 120 detected at block 410. In some examples, theprocessor 300 may determine a blob location for all the blobs 120detected at block 410, while in other examples, the processor 300 maydetermine a blob location for a subset of the blobs 120 detected atblock 410. The selection of the subset may be based, for example, on aspatial arrangement of the blobs 120 within the test pattern. That is,since each patch location is known, and since the blobs 120 are locatedat predetermined positions within its corresponding patch, the processor300 may determine the blob location for each blob 120.

At block 430, the processor 300 determines a calibration parameter forthe projector 104. In particular, the processor 300 uses the bloblocation and a detected attribute of at least one blob 210 which wasdetected at block 410 and located at block 425. Preferably, theprocessor 300 may determine the calibration parameter based on all ofthe blobs 210 in order to allow the calibration parameters to be betterlocalized and more accurate across the test pattern and the projectionarea for the projector 104. For example, the calibration parameter maybe a color or luminance of the projector 104. Generally, the processor300 may use the blob location to determine the input parameters for agiven blob 120 and compare the input parameters to the correspondingdetected attributes (e.g., color, luminance, geometric alignment) andcompute a correction to allow the projector 104 to project the givenblob 120 such that the detected attribute better approximates thedesired target parameter.

FIG. 9 depicts a flowchart of an example method 900 of determiningcalibration parameters for the projector 104.

At block 905, the processor 300 selects a blob 120 of the test pattern108. In particular, the processor 300 may select a blob 120 for which acalibration parameter or compensation has not yet been computed.

At block 910, the processor 300 obtains the target attribute for theblob 120 selected at block 905. For example, the target attribute may bea target color or luminance, as defined by the input color or luminanceof the blob 120 in the test pattern 108, or a geometric alignment, asdefined by the geometric properties of the test pattern 108. That is,the processor 300 may use the blob location of the blob 120 within thetest pattern 108 to identify the input attribute as the targetattribute.

At block 915, the processor 300 obtains the detected attribute for theblob 120 selected at block 905. That is, the processor 300 identifiesthe corresponding color or luminance of the blob 120 as detected by thecamera 116 in the captured image representing the test pattern 108 asprojected onto the surface 112. In some examples, the detected attributemay be sampled at the center of the blob 120, or at a predefined pointwithin the blob 120 (e.g., a predefined corner, etc.), while in otherexamples, the detected attribute may be an average of the detectedattribute at each point, or a selected subset of points, across the blob120.

At block 920, the processor 300 computes calibration parameters for theselected blob 120 based on the target attribute(s) determined at block910 and the detected attribute(s) determined at block 915. That is,based on the differences between the input to the projector 104 and thedetected output of the projection onto the surface 112, the processor300 may determine a compensation to adjust the input to the projector104 to allow the detected output attribute (i.e., as projected onto thesurface 112) to better approximate the target attribute. For example,the processor 300 may use standard radiometric or luminance compensationcomputations and/or geometric alignment computations, as will beunderstood by those of skill in the art, to define the calibrationparameters for the blob 120.

At block 925, the processor 300 determines whether there are any furtherblobs 120 in the test pattern 108 for which the calibration parametershave not yet been computed. If the determination at block 925 isaffirmative, the processor 300 returns to block 905 to select asubsequent blob 120 for which the calibration parameters have not yetbeen computed.

If the determination at block 925 is negative, that is that thecalibration parameters have been computed for each blob 120 in the testpattern 108, then the processor 300 proceeds to block 930. At block 930,the processor 300 smooths the calibration parameters of each of theblobs 120 over the projection area (i.e., over the area of the testpattern 108). In particular, the calibration parameters computed atblock 920 are individually computed per blob 120. Accordingly, adjacentblobs 120 may have different calibration parameters, which may causeabrupt and jarring changes between blobs 120 in the projection ifapplied per blob 120. Further, since the blobs 120 may be spaced apartfrom one another, the test pattern 108 may not produce a calibrationparameter for the negative spaces between blobs 120. Accordingly, ratherthan simply directly applying the calibration parameter over the blobarea of the given blob 120, the processor 300 may designate thecalibration parameter at a given point of the blob 120 (e.g., thecalibration parameter applies at the center of the blob 120) for each ofthe blobs 120 in the test pattern 108 and apply a smoothing function togenerate calibration parameters for the intermediary points between thegiven points of the blobs 120.

Returning to FIG. 4 , after determining the calibration parameters forthe projector 104, the processor 300 proceeds to block 435. At block435, the processor 300 applies the calibration parameters to calibratethe projector 104. That is, during a subsequent projection operation,the processor 300 may receive input data representing an image or videoto be projected by the projector 104, apply the calibration parametersto the input data to generate calibrated input data, and control thelight sources of the projector 104 to project the image or video inaccordance with the calibrated input data. In other examples, theapplication of the calibration parameters may be applied to the inputdata to generate calibrated input data prior to being received at theprojector 104 and/or the processor 300. Thus, the projector 104 willproject the image or video with the color, luminance, geometricalignment and/or other attributes adjusted to compensate for variationsand imperfections in the surface 112 to allow the projection to betterapproximate the original input data.

It will be appreciated that variations on the above method are alsocontemplated. For example, if a sufficient number of colors areemployed, and/or if the test pattern includes sufficiently few blobsand/or patches, the patch addresses may be encoded simply based on thecolors of the additional blobs in the patch, rather than based on anordered list of the colors of the additional blobs in the patch.

In some examples, rather than employing color blobs, the test patternmay include greyscale blobs including a predefined number of grey levels(e.g., 3 grey levels). In such examples, the grey blobs surrounding thewhite base blob may still encode the patch address, based on unique(unordered) combinations of the eight greyscale blobs. Such a testpattern may be advantageous, for example for applying only a luminancecorrection when a radiometric color compensation has already beenperformed against another projector.

In some examples, in order to best detect the blobs 120 and obtain amost accurate representation of the projected test pattern, the camera116 may additionally include an automatic exposure and/or focusadjustment capabilities. For example, referring to FIG. 10 , a flowchartof an example method 1000 of automatically adjusting camera parametersis depicted.

At block 1005, the projector 104 projects a test pattern, such as thetest pattern 108.

At block 1010, the camera 116 captures an image of the test pattern, ata first camera parameter. For example, the camera 116 may select a firstexposure and/or a first focus at which to capture the image.

At block 1015, the camera 116 selects a new camera parameter. Forexample, the camera 116 may select a different exposure and/or focus atwhich to capture a subsequent image. The camera parameter may beselected for example from a predefined list of camera parameters totest. Preferably, the camera 116 may only adjust one camera parameter ata time to better control the variables (i.e., only changing exposure orfocus, but not both).

The camera 116 may then return to block 1010 to capture a subsequentimage of the test pattern 108 at the new camera parameter.

If each of the camera parameters in the predefined list has been tested,the method 1000 proceeds to block 1020. At block 1020, the camera 116and/or the processor 300 and/or another suitable computing deviceselects an optimal camera parameter.

For example, the focus may be computed by using a mean-squared gradient(MSG) technique to compute the strength of the edges within the testpattern. Advantageously, the test pattern 108 may include blobs 120 withhigh contrast at all edges with the negative space between the blobs120, as based on the selection of primary, secondary, and tertiarycolors of the blobs 120. Accordingly, the focus of the camera 116 may beautomatically selected based on the focus with the highest MSG.

The exposure of the camera 116 may be computed based on the RGBcomponent values. In particular, since the test pattern 108 includeswhite blobs, the optimal exposure of the camera 116 may be selectedbased on the RGB component values of the white blobs. For example, thetarget or optimal exposure may result in RGB component values of thewhite blobs within a range of 245 to 255. In other examples, otherranges of acceptable RGB component values for the white blobs may beused.

At block 1025, after having selected the optimal camera parameters, thecamera 116 and/or the processor 300 may obtain an image under theselected optimal camera parameters. In some examples, when the camera116 has already captured an image of the test pattern under the selectedoptimal camera parameters, said image may simply be retrieved. In otherexamples, the camera 116 may capture a new image of the test patternunder the selected optimal camera parameters. The camera 116 maytherefore capture at least one image with optimized focus, exposureand/or other camera parameters. For example, the method 1000 may beperformed during block 405 of the method 400 to allow the image withoptimized focus and exposure to be used for the remainder of thecalibration procedure.

In other examples, the test pattern may include other features tooptimize camera exposure. For example, the test pattern may includevarying intensities of white (e.g., within a single blob, the outer edgemay have 100% intensity while the center of the blob has 10% intensity,different blobs may have different intensities, the test pattern mayinclude a 0 to 100% ramp-shaded region, or the like). The exposure ofthe camera may then be computed based on the relative number of 100%intense pixels, and/or the actual intensity of a designated exposurevalue (e.g., if the middle of the ramp-shaded region is supposed to be50% intensity, and it is either higher or lower intensity, thecorresponding exposure of the camera may be computed), or similar.

In some examples, in addition to optimizing the camera parameter(s), thesystem 100 may additionally optimize the focus and/or other parametersof the projector 104. For example, FIG. 11 a flowchart of an examplemethod 110 of automatically adjusting the focus of a projector ordisplay system is depicted.

At block 1105, the projector 104 projects a test pattern, such as thetest pattern 108, at a first focus. For example, the projector 104 mayselect a first focus at which to project the test pattern.

At block 1110, the camera 116 captures an image of the test pattern.

At block 1115, the projector 104 selects a new focus and/or otherprojector parameter. For example, the projector 104 may select adifferent focus at which to project the test pattern. The focus and/orother projector parameter may be selected from a predefined list ofprojector parameters to test. Preferably, the projector 104 may onlyadjust one projector parameter at a time, if multiple projectorparameters are being tested.

The projector 104 may then return to block 1105 to project the testpattern at the new projector parameter.

If each of the projector parameters or focus levels in the predefinedlist has been tested, the method 1100 proceeds to block 1120. At block1120, the projector 104 and/or another suitable computing device selectsan optimal focus and/or other projector parameter. For example, thefocus of the projector may similarly be computed using the MSG todetermine the strength of the edges within the test pattern. As will beappreciated, the test pattern 108 provides high contrast edges to allowthe focus of the projector 104 to be similarly optimized.

At block 1125, after having selected the optimal focus and/or otherprojector parameter, the camera 116 and/or the processor 300 may obtainan image with the selected optimal focus and/or projector parameter, forexample, by retrieving such an image if it has already been captured, orby projecting, using the projector 104, the test pattern with theoptimal focus and/or other projected parameter, and capturing anotherimage. The method 1100 may similarly be performed during block 405 ofthe method 400 to allow the image used for the remainder of thecalibration procedure to be optimized for projector focus. In otherexamples, the method 1100 may be performed after performance of themethod 400, since the projector focus may not materially affect thedetermination of the calibration parameters as much.

As described above, an example system and method of calibratingprojectors employs a test pattern which is organized into patchesincluding a white blob, and red, green and blue blobs to allowcalibration parameters, including radiometric or color compensation,luminance correction, and spatial alignment to be computed by projectinga single test pattern. In particular, in order to do so, the colors ofthe additional blobs in each patch define a patch address that allowsthe patch to be located within the test pattern. The location of eachpatch may then be used to compare the target attribute based on theinput at the given location, with the detected attribute, to computecalibration parameters to calibrate the projector and allow theprojector to compensate the projected image according to the targetsurface on which an image or video is projected.

The scope of the claims should not be limited by the embodiments setforth in the above examples, but should be given the broadestinterpretation consistent with the description as a whole.

1. A method of calibrating a display system, the method comprising:displaying a test pattern; identifying patches of the displayed testpattern, wherein each patch comprises a subset of the displayed testpattern; determining a patch location for at least one patch within thetest pattern based on detected attributes of the patch; determining acalibration parameter for the display system based on the patch locationand at least one of the detected attributes of the patch; andcalibrating the display system using the calibration parameter.
 2. Themethod of claim 1, wherein identifying patches of the test patterncomprises: detecting one or more base features in the displayed testpattern; and identifying the patches of the test pattern based on theone or more detected base features.
 3. The method of claim 2, whereindetecting the one or more base features comprises detecting a whitefeature in the displayed test pattern.
 4. The method of claim 1, whereindetermining the patch location comprises: determining a set of colorspresent in the patch; defining a patch address for the patch based onset of colors; and determining the patch location within the testpattern associated with the patch address.
 5. The method of claim 4,wherein each patch comprises a set of blobs, and wherein determining theset of colors comprises determining a blob color for each blob in thepatch.
 6. The method of claim 5, further comprising identifying a redreference blob, a green reference blob and a blue reference blob.
 7. Themethod of claim 6, further comprising ordering each additional blob inthe patch based on its spatial relationship within the patch to the redreference blob, the green reference blob, and the blue reference blob togenerate an ordered list of blob colors; and wherein the patch addressis defined based on the ordered list of blob colors.
 8. The method ofclaim 4, further comprising verifying the patch address against apredefined list of valid patch addresses; and when the patch address isnot a valid patch address, correcting the patch address based on thelist of valid patch addresses.
 9. The method of claim 1, furthercomprising: defining a macro-patch comprising two or more patches of thetest pattern; determining a hyper-address for the macro-patch, thehyper-addresses including respective patch addresses of the two or morepatches forming the hyper-address; verifying the hyper-address against apredefined list of valid hyper-addresses; and when the hyper-address isnot a valid hyper-address, determining a correct hyper-address andcorrecting at least one of the respective patch addresses of thehyper-address.
 10. The method of claim 1, wherein determining thecalibration parameters comprises: obtaining a target attribute for thedetected attribute of the patch based on the patch location; anddefining the calibration parameters based on a difference between thedetected attribute and the target attribute.
 11. A system comprising: adisplay system configured to project a test pattern onto a surface; acamera configured to capture an image of at least a portion of theprojected test pattern; and a processor configured to: identify, usingthe captured image, a patch of the test pattern, wherein the patchcomprises a subset of the test pattern; determine a patch location forthe patch within the test pattern based on detected attributes of thepatch; determine a calibration parameter for the display system based onthe patch location and one of the detected attributes of the patch; andcalibrate the display system using the calibration parameter.
 12. Thesystem of claim 11, wherein, to identify a patch of the test pattern,the processor is configured to: detect one or more base features in thetest pattern; and identify the patches of the test pattern based on theone or more detected base features.
 13. The system of claim 12, whereinthe one or more base features comprises a white feature in the testpattern.
 14. The system of claim 11, wherein to determine the patchlocation, the processor is configured to: determine a set of colorspresent in the patch; define a patch address for the patch based on setof colors; and determine the patch location within the test patternassociated with the patch address.
 15. The system of claim 14, whereineach patch comprises a set of blobs, and wherein the processor isconfigured to determine a blob color for each blob in the patch.
 16. Thesystem of claim 15, wherein the processor is configured to identify ared reference blob, a green reference blob and a blue reference blob.17. The system of claim 16, wherein the processor is configured to ordereach additional blob in the patch based on a spatial relationship withinthe patch to the red reference blob, the green reference blob, and theblue reference blob to generate an ordered list of blob colors; andwherein the patch address is defined based on the ordered list of blobcolors.
 18. The system of claim 14, wherein the processor is furtherconfigured to verify the patch address against a predefined list ofvalid patch addresses; and when the patch address is not a valid patchaddress, correct the patch address based on the list of valid patchaddresses.
 19. The system of claim 11, wherein the processor is furtherconfigured to: define a macro-patch comprising two or more patches ofthe test pattern; determine a hyper-address for the macro-patch, thehyper-addresses including respective patch addresses of the two or morepatches forming the hyper-address; verify the hyper-address against apredefined list of valid hyper-addresses; and when the hyper-address isnot a valid hyper-address, determine a correct hyper-address and correctat least one of the respective patch addresses of the hyper-address. 20.The system of claim 11, wherein to determine the calibration parameter,the processor is configured to: obtain a target attribute for thedetected attribute of the patch based on the patch location; and definethe calibration parameters based on a difference between the detectedattribute and the target attribute.
 21. The system of claim 11, whereinthe camera is integrated with the display system.